HLAProfiler utilizes k-mer profiles to improve HLA calling accuracy for rare and common alleles in RNA-seq data

BACKGROUND: The human leukocyte antigen (HLA) system is a genomic region involved in regulating the human immune system by encoding cell membrane major histocompatibility complex (MHC) proteins that are responsible for self-recognition. Understanding the variation in this region provides important insights into autoimmune disorders, disease susceptibility, oncological immunotherapy, regenerative medicine, transplant rejection, and toxicogenomics. Traditional approaches to HLA typing are low throughput, target only a few genes, are labor intensive and costly, or require specialized protocols. RNA sequencing promises a relatively inexpensive, high-throughput solution for HLA calling across all genes, with the bonus of complete transcriptome information and widespread availability of historical data. Existing tools have been limited in their ability to accurately and comprehensively call HLA genes from RNA-seq data. RESULTS: We created HLAProfiler ( https://github.com/ExpressionAnalysis/HLAProfiler ), a k-mer profile-based method for HLA calling in RNA-seq data which can identify rare and common HLA alleles with > 99% accuracy at two-field precision in both biological and simulated data. For 68% of novel alleles not present in the reference database, HLAProfiler can correctly identify the two-field precision or exact coding sequence, a significant advance over existing algorithms. CONCLUSIONS: HLAProfiler allows for accurate HLA calls in RNA-seq data, reliably expanding the utility of these data in HLA-related research and enabling advances across a broad range of disciplines. Additionally, by using the observed data to identify potential novel alleles and update partial alleles, HLAProfiler will facilitate further improvements to the existing database of reference HLA alleles. HLAProfiler is available at https://expressionanalysis.github.io/HLAProfiler/

From flavors and pharmaceuticals to advanced biofuels: Production of isoprenoids in Saccharomyces cerevisiae

Isoprenoids denote the largest group of chemicals in the plant kingdom and are employed for a wide range of applications in the food and pharmaceutical industry. In recent years, isoprenoids have additionally been recognized as suitable replacements for petroleum-derived fuels and could thus promote the transition towards a more sustainable society. To realize the biofuel potential of isoprenoids, a very efficient production system is required. While complex chemical structures as well as the low abundance in nature demonstrate the shortcomings of chemical synthesis and plant extraction, isoprenoids can be produced by genetically engineered microorganisms from renewable carbon sources. In this article, we summarize the development of isoprenoid applications from flavors and pharmaceuticals to advanced biofuels and review the strategies to design microbial cell factories, focusing on Saccharomyces cerevisiae for the production of these compounds. While the high complexity of biosynthetic pathways and the toxicity of certain isoprenoids still denote challenges that need to be addressed, metabolic engineering has enabled large-scale production of several terpenoids and thus, the utilization of these compounds is likely to expand in the future

Why are “others” so polarized? Perceived political polarization and media use in 10 countries.

This study tests the associations between news media use and perceived political polarization, conceptualized as citizens’ beliefs about partisan divides among major political parties. Relying on representative surveys in Canada, Colombia, Greece, India, Italy, Japan, South Korea, Norway, United Kingdom and United States, we test whether perceived polarization is related to the use of television news, newspaper, radio news, and online news media. Data show that online news consumption is systematically and consistently related to perceived polarization, but not to attitude polarization, understood as individual attitude extremity. In contrast, the relationships between traditional media use and perceived and attitude polarization is mostly country dependent. An explanation of these findings based on exemplification is proposed and tested in an experimental design

HLAProfiler utilizes k-mer profiles to improve HLA calling accuracy for rare and common alleles in RNA-seq data

Abstract Background The human leukocyte antigen (HLA) system is a genomic region involved in regulating the human immune system by encoding cell membrane major histocompatibility complex (MHC) proteins that are responsible for self-recognition. Understanding the variation in this region provides important insights into autoimmune disorders, disease susceptibility, oncological immunotherapy, regenerative medicine, transplant rejection, and toxicogenomics. Traditional approaches to HLA typing are low throughput, target only a few genes, are labor intensive and costly, or require specialized protocols. RNA sequencing promises a relatively inexpensive, high-throughput solution for HLA calling across all genes, with the bonus of complete transcriptome information and widespread availability of historical data. Existing tools have been limited in their ability to accurately and comprehensively call HLA genes from RNA-seq data. Results We created HLAProfiler ( https://github.com/ExpressionAnalysis/HLAProfiler ), a k-mer profile-based method for HLA calling in RNA-seq data which can identify rare and common HLA alleles with > 99% accuracy at two-field precision in both biological and simulated data. For 68% of novel alleles not present in the reference database, HLAProfiler can correctly identify the two-field precision or exact coding sequence, a significant advance over existing algorithms. Conclusions HLAProfiler allows for accurate HLA calls in RNA-seq data, reliably expanding the utility of these data in HLA-related research and enabling advances across a broad range of disciplines. Additionally, by using the observed data to identify potential novel alleles and update partial alleles, HLAProfiler will facilitate further improvements to the existing database of reference HLA alleles. HLAProfiler is available at https://expressionanalysis.github.io/HLAProfiler/

Additional file 1: Tables and Figure. of HLAProfiler utilizes k-mer profiles to improve HLA calling accuracy for rare and common alleles in RNA-seq data

Table S1. Counts for each population of real samples used in the current study. Table S2. Number of alleles available and used for each type of simulated data set. Table S8. Novel alleles identified by TruSight HLA for 33 HapMap samples having discordances between Sanger sequencing and RNA sequencing. Table S9. Accuracy of KIR genotyping at two-field precision. Figure S1. Representative HLA taxonomy. Figure S2. Run times of HLA calling software. Figure S3. Accuracy of HLAProfiler at low number of reads. Figure S4. Sequencing read count and KIR genotyping accuracy. (PDF 584 kb

Additional file 2: Table S3–S7. of HLAProfiler utilizes k-mer profiles to improve HLA calling accuracy for rare and common alleles in RNA-seq data

Table S3. Accuracy of HLA calling in simulated data. Table S4. Concordance of HLA calling with "gold standard" in Guevadis data.  Table S5. Call rate for all methods across all genes for Guevadis data set. Table S6. Discrepant calls resolved with TruSight HLA typing. Table S7. Comparison of predicted sequence to the truth sequence in novel allele dataset. (XLSX 67 kb

Differences in graph theory functional connectivity in left and right temporal lobe epilepsy

PURPOSE: To investigate lateralized differences in limbic system functional connectivity between left and right temporal lobe epilepsy (TLE) using graph theory. METHODS: Interictal resting state fMRI was performed in 14 left TLE patients, 11 right TLE patients, and 12 controls. Graph theory analysis of 10 bilateral limbic regions of interest was conducted. Changes in edgewise functional connectivity, network topology, and regional topology were quantified, and then left and right TLE were compared. RESULTS: Limbic edgewise functional connectivity was predominantly reduced in both left and right TLE. More regional connections were reduced in right TLE, most prominently involving reduced interhemispheric connectivity between the bilateral insula and bilateral hippocampi. A smaller number of limbic connections were increased in TLE, more so in left than in right TLE. Topologically, the most pronounced change was a reduction in average network betweenness centrality and concurrent increase in left hippocampal betweenness centrality in right TLE. In contrast, left TLE exhibited a weak trend toward increased right hippocampal betweenness centrality, with no change in average network betweenness centrality. CONCLUSION: Limbic functional connectivity is predominantly reduced in both left and right TLE, with more pronounced reductions in right TLE. In contrast, left TLE exhibits both edgewise and topological changes that suggest a tendency toward reorganization. Network changes in TLE and lateralized differences thereof may have important diagnostic and prognostic implications

Effects of early life NICU stress on the developing gut microbiome

Succession of gut microbial community structure for newborns is highly influenced by early life factors. Many preterm infants cared for in the NICU are exposed to parent–infant separation, stress, and pain from medical care procedures. The purpose of the study was to investigate the impact of early life stress on the trajectory of gut microbial structure. Stool samples from very preterm infants were collected weekly for 6 weeks. NICU stress exposure data were collected daily for 6 weeks. V4 region of the 16S rRNA gene was amplified by PCR and sequenced. Zero-inflated beta regression model with random effects was used to assess the impact of stress on gut microbiome trajectories. Week of sampling was significant for Escherichia, Staphylococcus, Enterococcus, Bifidobacterium, Proteus, Streptococcus, Clostridium butyricum, and Clostridium perfringens. Antibiotic usage was significant for Proteus, Citrobacter, and C. perfringens. Gender was significant for Proteus. Stress exposure occurring 1 and 2 weeks prior to sampling had a significant effect on Proteus and Veillonella. NICU stress exposure had a significant effect on Proteus and Veillonella. An overall dominance of Gammaproteobacteria was found. Findings suggest early life NICU stress may significantly influence the developing gut microbiome, which is important to NICU practice and future microbiome research